Reflect: How Hindsight Reasons with Disposition

When you call reflect(), Hindsight doesn't just retrieve facts — it reasons about them through the lens of the bank's unique disposition, forming new opinions and generating contextual responses.

Why Reflect?

Most AI systems can retrieve facts, but they can't reason about them in a consistent way. Every response is generated fresh without a stable perspective or evolving beliefs.

The Problem

Without reflect:

No consistent character: "Should we adopt remote work?" gets a different answer each time based on the LLM's randomness
No opinion formation: The system never develops beliefs based on accumulated evidence
No reasoning context: Responses don't reflect what the bank has learned or its perspective
Generic responses: Every AI sounds the same — no disposition, no point of view

The Value

With reflect:

Consistent character: A bank configured as "detail-oriented, cautious" will consistently emphasize risks and thorough planning
Evolving opinions: As the bank learns more about a topic, its opinions strengthen, weaken, or change — just like a real expert
Contextual reasoning: Responses reflect the bank's accumulated knowledge and perspective: "Based on what I know about your team's remote work success..."
Differentiated behavior: Customer support bots sound diplomatic, code reviewers sound direct, creative assistants sound open-minded

When to Use Reflect

Use `recall()` when...	Use `reflect()` when...
You need raw facts	You need reasoned interpretation
You're building your own reasoning	You want disposition-consistent responses
You need maximum control	You want the bank to "think" for itself
Simple fact lookup	Forming recommendations or opinions

Example:

recall("Alice") → Returns all Alice facts
reflect("Should we hire Alice?") → Reasons about Alice's fit based on accumulated knowledge, weighs evidence, forms opinion

The Reflect Process

Recall relevant memories based on the query
Load the bank's disposition traits and background
Reason about the memories through the disposition lens
Form new opinions with confidence scores
Return response, sources, and any new beliefs

Disposition Framework (CARA)

When you create a memory bank, you can configure its disposition using Big Five traits. These traits influence how the bank interprets information and forms opinions:

You can also provide a natural language background that describes the bank's identity and perspective, which shapes how these traits are applied.

Trait	Low	High
Openness	Prefers proven methods	Embraces new ideas
Conscientiousness	Flexible, spontaneous	Systematic, organized
Extraversion	Independent	Collaborative
Agreeableness	Direct, analytical	Diplomatic, harmonious
Neuroticism	Calm, optimistic	Risk-aware, cautious

Background: Natural Language Identity

Beyond numeric traits, you can provide a natural language background that describes the bank's identity:

client.create_bank(
    bank_id="my-bank",
    background="I am a senior software architect with 15 years of distributed "
               "systems experience. I prefer simplicity over cutting-edge technology.",
    disposition={
        "openness": 0.3,  # Prefers proven methods
        "conscientiousness": 0.9,  # Highly organized
        # ... other traits
    }
)

The background provides context that shapes how disposition traits are applied:

"I prefer simplicity" + low openness → consistently favors established solutions
"15 years experience" → responses reference this expertise
First-person perspective → creates consistent voice

Bias Strength

The bias_strength parameter (0-1) controls how much disposition influences reasoning:

0.0: Purely evidence-based
0.5: Balanced disposition and evidence
1.0: Strongly disposition-driven

Opinion Formation

When reflect() encounters a question that warrants forming an opinion, disposition shapes the response.

Same Facts, Different Opinions

Two banks with different dispositions, given identical facts about remote work:

Bank A (high openness, low conscientiousness):

"Remote work unlocks creative flexibility and spontaneous innovation. The freedom to work from anywhere enables breakthrough thinking."

Bank B (low openness, high conscientiousness):

"Remote work lacks the structure and accountability needed for consistent performance. In-person collaboration is more reliable."

Same facts → Different conclusions because disposition shapes interpretation.

Opinion Evolution

Opinions aren't static — they evolve as new evidence arrives. Here's a real-world example with a database library:

Event	What the bank learns	Opinion formed
Day 1	"Redis is open source under BSD license"	"Redis is excellent for caching — fast, reliable, and OSS-friendly" (confidence: 0.85)
Day 2	"Redis has great community support and documentation"	Opinion reinforced (confidence: 0.90)
Day 30	"Redis changed license to SSPL, restricting cloud usage"	"Redis is still technically strong, but license concerns for cloud deployments" (confidence: 0.65)
Day 45	"Valkey forked Redis under BSD license with Linux Foundation backing"	"Consider Valkey for new projects requiring true OSS; Redis for existing deployments" (confidence: 0.80)

Before the license change:

"Should we use Redis for our caching layer?" → "Yes, Redis is the industry standard — fast, battle-tested, and fully open source."

After the license change:

"Should we use Redis for our caching layer?" → "It depends. For cloud deployments, consider Valkey (the BSD-licensed fork). For on-premise, Redis remains excellent technically."

This continuous learning ensures recommendations stay current with real-world changes.

Disposition Presets by Use Case

Different use cases benefit from different disposition configurations:

Use Case	Recommended Traits	Why
Customer Support	High agreeableness Low neuroticism	Diplomatic, calm under pressure
Code Review	High conscientiousness Low agreeableness	Detail-oriented, direct feedback
Creative Writing	High openness High extraversion	Embraces novelty, expressive
Risk Analysis	High neuroticism High conscientiousness	Risk-aware, methodical
Research Assistant	High openness High conscientiousness	Curious, thorough

What You Get from Reflect

When you call reflect():

Returns:

Response text — Disposition-influenced answer
Based on — Which memories were used (with relevance scores)

Example:

{
  "text": "Based on Alice's ML expertise and her work at Google, she'd be an excellent fit for the research team lead position...",
  "based_on": {
    "world": [
      {"text": "Alice works at Google...", "weight": 0.95},
      {"text": "Alice specializes in ML...", "weight": 0.88}
    ]
  }
}

Note: New opinions are formed asynchronously in the background. They'll influence future reflect() calls but aren't returned directly.

Why Disposition Matters

Without disposition, all AI assistants sound the same. With disposition:

Customer support bots can be diplomatic and empathetic
Code review assistants can be direct and thorough
Creative assistants can be open to unconventional ideas
Risk analysts can be appropriately cautious

Disposition creates consistent character across conversations while allowing opinions to evolve with evidence.

Next Steps

Retain — How rich facts are stored
Recall — How multi-strategy search works
API Reference: Reflect — Code examples and usage

Why Reflect?​

The Problem​

The Value​

When to Use Reflect​

The Reflect Process​

Disposition Framework (CARA)​

Background: Natural Language Identity​

Bias Strength​

Opinion Formation​

Same Facts, Different Opinions​

Opinion Evolution​

Disposition Presets by Use Case​

What You Get from Reflect​

Why Disposition Matters​

Next Steps​